How to Create ORB-SLAM3 ROS2 Environment on Raspberry Pi 5 and Ubuntu ARM 22.04

This step-by-step guide will walk you through the process of seamlessly integrating ORB-SLAM3 into your ROS2 environment. It is based on Ubuntu 22.04 and assumes you have a few key packages already installed.

Environment Setup

• Operating System: Ubuntu 22.04

• Essential Packages:

  • Eigen v3.4.0: Ensure you have this specific version installed to avoid compatibility issues.

  • OpenCV v4.5.4: This is the version I'm using in my setup, but later versions should also work.

  • ROS2 Humble: If you haven't installed ROS2 Humble yet, you can follow my installation guide here: [Insert Link to your ROS2 Humble Installation Guide]

Step 1: Installing cv_bridge

The cv_bridge package is essential for converting between OpenCV image formats and ROS2 message types. You can install it using the following command:

      sudo apt install ros-humble-cv-bridge -y
    

Step 2: Installing Pangolin

Pangolin is a powerful and efficient OpenGL-based library for 3D visualization. It's a popular choice for visual SLAM applications like ORB-SLAM3 because of its lightweight design, efficient performance, and user-friendly interface. It provides a framework for rendering 3D scenes, handling input events, and displaying data in a visually appealing manner.

To install Pangolin, you can either build it from source or download a pre-built package.

Building Pangolin from Source

1. Download the source code:

         git clone https://github.com/stevenlovegrove/Pangolin.git
       

2. Navigate to the Pangolin directory:

         cd Pangolin
       

3. Configure and build Pangolin:

   mkdir build && cd build
   cmake .. -DPANGOLIN_BUILD_EXAMPLES=ON
   make -j$(nproc)
       

   
   

Installing a Pre-built Package

Alternatively, you can install Pangolin using a pre-built package. The availability of pre-built packages may vary depending on your distribution. Check the Pangolin repository for more information.

Step 3: Installing ORB-SLAM3

ORB-SLAM3 is a state-of-the-art visual SLAM system that supports both monocular and stereo cameras. It's known for its robustness, accuracy, and ability to handle challenging environments.

Downloading ORB-SLAM3:

1. Download the source code:

         git clone https://github.com/UZ-SLAMLab/ORB_SLAM3.git
       

   
   

2. Navigate to the ORB-SLAM3 directory:

         cd ORB_SLAM3
       

   
   

Compiling ORB-SLAM3:

1. Install necessary dependencies:

         sudo apt-get install libsuitesparse-dev libopenblas-dev libeigen3-dev libopencv-dev
       

   
   

2. Compile the ORB-SLAM3 library:

   mkdir build && cd build
   cmake ..
   make -j$(nproc)
       

   
   

Step 4: Setting up ROS2 Node for ORB-SLAM3

Now that you have ORB-SLAM3 installed, you need to create a ROS2 node that will use it for SLAM. This node will receive camera data from your robot and process it using ORB-SLAM3 to create a map of the environment and track the robot's position.

Creating the ROS2 Node:

1. Create a ROS2 package:

         ros2 pkg create orb_slam3_node --build-type ament_python
       

   
   

2. Navigate to the package directory:

         cd orb_slam3_node
       

   
   

3. Create a Python script for your ROS2 node:

         touch orb_slam3_node.py
       

   
   

Writing the Python Code:

Within the orb_slam3_node.py file, you'll write the code to:

1. Initialize ROS2 node:

   import rclpy
   from rclpy.node import Node
   
   class ORB_SLAM3_Node(Node):
       def __init__(self):
           super().__init__('orb_slam3_node')
       

   
   

2. Subscribe to camera topic:

   self.camera_subscriber = self.create_subscription(
       Image, 'camera_topic', self.camera_callback, 10
   )
       

   
   

3. Initialize ORB-SLAM3:

   # Load ORB-SLAM3 configuration file
   orb_slam3_config = "/path/to/your/orb_slam3_config.yaml" 
   self.orb_slam3 = ORB_SLAM3(orb_slam3_config)
       

   
   

4. Process camera data in callback function:

       def camera_callback(self, msg):
       # Convert ROS2 Image message to OpenCV image format
       cv_image = cv_bridge.imgmsg_to_cv2(msg, "bgr8")
   
       # Process image with ORB-SLAM3
       self.orb_slam3.process_image(cv_image)
   
       # Publish SLAM information (e.g., robot pose, map data)
       # ...
       

   
   

Step 5: Running the ROS2 Node

1. Build the ROS2 package:

         colcon build --packages-select orb_slam3_node
       

   
   

2. Launch the ROS2 node:

         ros2 launch orb_slam3_node orb_slam3_node.launch.py
       

   
   

Step 6: Testing and Debugging

1. Ensure camera data is being published on the correct topic.

2. Check for any errors in the ROS2 logs.

3. Visualize SLAM output using RViz or other visualization tools.

Troubleshooting Tips

• Make sure the correct versions of Eigen, OpenCV, and other dependencies are installed.

• Verify that the ROS2 node is subscribing to the correct camera topic.

• Check the ORB-SLAM3 configuration file for any errors.

• Use debugging tools like print statements or ROS2 logging to pinpoint the issue.

Conclusion

Integrating ORB-SLAM3 into your ROS2 node can be a rewarding experience, enabling you to build robust and accurate SLAM applications for your robots. By following these steps and addressing any challenges along the way, you can successfully leverage the power of ORB-SLAM3 in your ROS2 environment. Remember to test your implementation thoroughly and debug any issues to ensure your SLAM system functions as expected.